{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making Features Discrete\n",
    "In previous weeks we have imputed values and made new variables using the `pandas.cut` function to define how to make a single attribute discrete. In this module, let's instead use clustering to convert one or more features into discrete, categorical features (integers). \n",
    "\n",
    "The process will be simple:\n",
    "- Choose a subset of features from the dataset to cluster upon\n",
    "- Cluster the features assuming according to a given algorithm\n",
    "- Replace the features with their discrete cluster labels as a form of discretization\n",
    "- Perform classification using the new feature from the dataset\n",
    "\n",
    "In this notebook, we will investigate simple clustering methods for making the clusters discrete: kmeans, hierarchical agglomerative clustering, and DBSCAN. The dataset we will use comes from our titanic dataset that we have used in the past."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 882 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      "Survived      882 non-null int64\n",
      "Age           882 non-null float64\n",
      "Parch         882 non-null float64\n",
      "SibSp         882 non-null float64\n",
      "Pclass        882 non-null float64\n",
      "Fare          882 non-null float64\n",
      "Embarked_C    882 non-null float64\n",
      "Embarked_Q    882 non-null float64\n",
      "Embarked_S    882 non-null float64\n",
      "IsMale        882 non-null float64\n",
      "FamilySize    882 non-null float64\n",
      "dtypes: float64(10), int64(1)\n",
      "memory usage: 82.7 KB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from __future__ import print_function\n",
    "\n",
    "df = pd.read_csv('data/titanic.csv') # read in the csv file\n",
    "\n",
    "# 1. Remove attributes that just arent useful for us\n",
    "del df['PassengerId']\n",
    "del df['Name']\n",
    "del df['Cabin']\n",
    "del df['Ticket']\n",
    "\n",
    "# 2. Impute some missing values, grouped by their Pclass and SibSp numbers\n",
    "df_grouped = df.groupby(by=['Pclass','SibSp'])\n",
    "\n",
    "# # now use this grouping to fill the data set in each group, then transform back\n",
    "# fill in the numeric values\n",
    "df_imputed = df_grouped.transform(lambda grp: grp.fillna(grp.median()))\n",
    "# fill in the categorical values\n",
    "df_imputed[['Sex','Embarked']] = df_grouped[['Sex','Embarked']].apply(lambda grp: grp.fillna(grp.mode()))\n",
    "# fillin the grouped variables from original data frame\n",
    "df_imputed[['Pclass','SibSp']] = df[['Pclass','SibSp']]\n",
    "\n",
    "# 4. drop rows that still had missing values after grouped imputation\n",
    "df_imputed.dropna(inplace=True)\n",
    "\n",
    "# 5. Rearrange the columns\n",
    "df_imputed = df_imputed[['Survived','Age','Sex','Parch','SibSp','Pclass','Fare','Embarked']]\n",
    "\n",
    "# perform one-hot encoding of the categorical data \"embarked\"\n",
    "tmp_df = pd.get_dummies(df_imputed.Embarked,prefix='Embarked')\n",
    "df_imputed = pd.concat((df_imputed,tmp_df),axis=1) # add back into the dataframe\n",
    "\n",
    "# replace the current Sex atribute with something slightly more intuitive and readable\n",
    "df_imputed['IsMale'] = df_imputed.Sex=='male' \n",
    "df_imputed.IsMale = df_imputed.IsMale.astype(np.int)\n",
    "\n",
    "# Now let's clean up the dataset\n",
    "if 'Sex' in df_imputed:\n",
    "    del df_imputed['Sex'] # if 'Sex' column still exists, delete it (as we created an ismale column)\n",
    "    \n",
    "if 'Embarked' in df_imputed:    \n",
    "    del df_imputed['Embarked'] # get reid of the original category as it is now one-hot encoded\n",
    "\n",
    "# Finally, let's create a new variable based on the number of family members\n",
    "# traveling with the passenger\n",
    "\n",
    "# notice that this new column did not exist before this line of code--we use the pandas \n",
    "#    syntax to add it in \n",
    "df_imputed['FamilySize'] = df_imputed.Parch + df_imputed.SibSp\n",
    "\n",
    "y = df_imputed['Survived']\n",
    "df_imputed = (df_imputed-df_imputed.mean())/df_imputed.std()\n",
    "df_imputed['Survived'] = y\n",
    "\n",
    "df_imputed.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this dataset, it probably makes sense to try and cluster `PClass` and `Fare` together because they have similar information and can likely be combined. It is unclear exactly where to make the classes discrete and how many levels we should make, so we will try a few different parameterizations to investigate this.\n",
    "\n",
    "It also might make sense to make the `Age`, `Parch`, and `SibSp` cariables into a single discrete variable representing clusters of families. Again, we will need to try different parameterizations (numbers of cluster and the algorithm for clustering).\n",
    "\n",
    "## Baseline Classification Performance\n",
    "Let's start by performing 10 fold cross validation and using the raw features in a Random Forest classifer. Let's get the average accuracy of classifying whther a person survives or does not from the Titanic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy =  80.5120020429 +- 5.18006400357\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "y = df_imputed['Survived']\n",
    "X = df_imputed[['Age','IsMale','Parch','SibSp','Pclass','Fare']]\n",
    "cv = StratifiedKFold(n_splits=10)\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=150,random_state=1)\n",
    "\n",
    "acc = cross_val_score(clf,X,y=y,cv=cv)\n",
    "\n",
    "print (\"Average accuracy = \", acc.mean()*100, \"+-\", acc.std()*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, now let's start with a bit of feature engineering. We will start by using kmeans on `PClass` and `Fare` together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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XsiTRaKqsSHb1andZR9kTEREZLWVOppj8Tpk1a0JlXWJXrOgFHG67rZv77ovh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\n4T4duqKi61WZ/cDPuTBKqcElEI/rB0+GzwOv7dixg7a2Nq/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sXfvYxNRTY8y/YXM1ulLj\nfJ0ObHZ5fLvLNsjomeihauzFPVMiENrRi2Puzl3Y3JZzgedFpAnAGJPr9eVyKzbZ9asiktlrVAOs\nww5buQU8rcBOYB9jTEBEUqcOj8hxvmHOc5QasDQhVqmBaQz24nZXRk2Mrzpfu/rbfwV70TxIRP6U\n+Ied7XMzcJjbk5wcjH8CB/eiveuA8c7MllQXAP8UEbdelT31ZeBFEVmdEph8AdiX9Pcn7vZkZ/+L\nsHkl/5k6LJNiHXZIbXPGe3kRNgcnBjyPvVH8RspxC7EBj5uDsPk6Sg1Y2nOi1MD0FnaY40fGmA7s\nsMK5wCzn8dJcTxSRamPMfOAmY8wQ4CXsBfFnQAfuOR8JldgZNz11OzYQed4Y81NsL8wMbGLqxb04\nHux+aOb3wDeNMbOxOTSfA36EDcJS3x/X4xhjTsDOXnoJeM5JzE3d90+kv67bsK9rGvbncCWAiLxg\njKnEJhuPwgYe38MGSW69OOOBO3fz2pTKa9pzolR+ynk3DyAiddiaGQHsrJMHsQHGRKDe+ZrzWE7Z\n+auBqdhE2JuxvQAniUh9F6d+HDjGqVmyu/Ymp+WKyHbsRfc17HDLCqe9XxORB92e0w25zplwNfBb\n4CbslOOZzv8fAL7k5JK4HSfx/WTsFOaTnHZvxOasJP7t70ypHo/NZ7kXmzR7HDBTRO5OOeZUbPLv\nT7H5Kx/gMm3bCYBG0DmFWqkBKRCPd/fvXCmlds8Yswl4XETmed2WgcYYsxgYJiJne90WpfYm7TlR\nSvW164BvG2NyDh2pnnPycaai9U3UIKDBiVKqT4nIGuBJbFl41Xd+AdysZevVYKDDOkoppZTyFe05\nUUoppZSvaHCilFJKKV/R4EQppZRSvqLBiVJKKaV8RYMTpZRSSvmKBidKKaWU8hUNTpRSSinlKxqc\nKKWUUspX/g+ZUETmw1urNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ef21780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.style.use(\"ggplot\")\n",
    "\n",
    "%matplotlib inline\n",
    "X1 = df_imputed[['Pclass','Fare']].values\n",
    "\n",
    "plt.scatter(X1[:, 1], X1[:, 0]+np.random.random(X1[:, 1].shape)/2, \n",
    "             s=20)\n",
    "plt.xlabel('Fare (normalized)'), plt.ylabel('Class')\n",
    "plt.grid()\n",
    "plt.title('Class Versus Fare')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy (with kmeans for class/fare)=  79.9438202247 +- 4.40761643373\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "X1 = df_imputed[['Pclass','Fare']]\n",
    "\n",
    "cls = KMeans(n_clusters=8, init='k-means++',random_state=1)\n",
    "cls.fit(X1)\n",
    "newfeature = cls.labels_ # the labels from kmeans clustering\n",
    "\n",
    "y = df_imputed['Survived']\n",
    "X = df_imputed[['Age','IsMale','Parch','SibSp']]\n",
    "X = np.column_stack((X,pd.get_dummies(newfeature)))\n",
    "\n",
    "acc = cross_val_score(clf,X,y=y,cv=cv)\n",
    "\n",
    "print (\"Average accuracy (with kmeans for class/fare)= \", acc.mean()*100, \"+-\", acc.std()*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it seems that the average accuracy of the folds has stayed about the same, but the deviation from the mean has been considerably decreased. Let's now try adding in different discretization of the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy (with kmeans for Age/Family)=  80.1621552605 +- 3.75674361296\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import numpy as np\n",
    "\n",
    "X2 = df_imputed[['Age','Parch','SibSp']]\n",
    "\n",
    "cls = KMeans(n_clusters=8, init='k-means++',random_state=1)\n",
    "cls.fit(X2)\n",
    "newfeature = cls.labels_ # the labels from kmeans clustering\n",
    "\n",
    "y = df_imputed['Survived']\n",
    "X = df_imputed[['IsMale','Pclass','Fare']]\n",
    "X = np.column_stack((X,pd.get_dummies(newfeature)))\n",
    "\n",
    "acc = cross_val_score(clf,X,y=y,cv=cv)\n",
    "\n",
    "print (\"Average accuracy (with kmeans for Age/Family)= \", acc.mean()*100, \"+-\", acc.std()*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This discretization actually helps increase the accuracy on average, but not really helping in the lowering of the deviation from the mean. What if we combine the different clusterings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy =  80.5005107252 +- 3.78629568705\n"
     ]
    }
   ],
   "source": [
    "# get the first clustering\n",
    "cls_fare = KMeans(n_clusters=8, init='k-means++',random_state=1)\n",
    "cls_fare.fit(X1)\n",
    "newfeature_fare = cls_fare.labels_ # the labels from kmeans clustering\n",
    "\n",
    "# append on the second clustering\n",
    "cls_fam = KMeans(n_clusters=8, init='k-means++',random_state=1)\n",
    "cls_fam.fit(X2)\n",
    "newfeature_fam = cls_fam.labels_ # the labels from kmeans clustering\n",
    "\n",
    "y = df_imputed['Survived']\n",
    "X = df_imputed[['IsMale']]\n",
    "X = np.column_stack((X,pd.get_dummies(newfeature_fare),pd.get_dummies(newfeature_fam)))\n",
    "\n",
    "acc = cross_val_score(clf,X,y=y,cv=cv)\n",
    "\n",
    "print (\"Average accuracy = \", acc.mean()*100, \"+-\", acc.std()*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems this is not quite as accurate, but we still need to vary the parameters and see what works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clusters 4 16 Average accuracy =  79.9361593463 +- 3.174744809\n",
      "Clusters 4 17 Average accuracy =  80.2757916241 +- 2.46520037533\n",
      "Clusters 4 18 Average accuracy =  80.1647088866 +- 3.29652885233\n",
      "Clusters 5 16 Average accuracy =  80.6179775281 +- 3.80853029585\n",
      "Clusters 5 17 Average accuracy =  80.9576098059 +- 3.20908722865\n",
      "Clusters 5 18 Average accuracy =  80.7316138917 +- 3.62224668263\n",
      "Clusters 6 16 Average accuracy =  80.2770684372 +- 3.18094627365\n",
      "Clusters 6 17 Average accuracy =  80.8414198161 +- 3.25466264618\n",
      "Clusters 6 18 Average accuracy =  80.1634320735 +- 2.97899507801\n",
      "Clusters 7 16 Average accuracy =  80.1659856997 +- 3.51247633398\n",
      "Clusters 7 17 Average accuracy =  80.5043411645 +- 3.40245386722\n",
      "Clusters 7 18 Average accuracy =  79.8237997957 +- 3.08129572204\n",
      "Clusters 8 16 Average accuracy =  79.4841675179 +- 3.49647654759\n",
      "Clusters 8 17 Average accuracy =  79.932328907 +- 3.6601492037\n",
      "Clusters 8 18 Average accuracy =  79.143258427 +- 3.70756815691\n",
      "Clusters 9 16 Average accuracy =  79.5978038815 +- 3.20770641168\n",
      "Clusters 9 17 Average accuracy =  80.0485188968 +- 3.46977658923\n",
      "Clusters 9 18 Average accuracy =  79.2581716037 +- 3.40143218742\n",
      "CPU times: user 43.3 s, sys: 161 ms, total: 43.5 s\n",
      "Wall time: 43.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "X1 = df_imputed[['Pclass','Fare']]\n",
    "X2 = df_imputed[['Age','Parch','SibSp']]\n",
    "\n",
    "params = []\n",
    "for n_fare in range(4,10):\n",
    "    for n_fam in range(16,19):\n",
    "        # get the first clustering\n",
    "        cls_fare = KMeans(n_clusters=n_fare, init='k-means++',random_state=1)\n",
    "        cls_fare.fit(X1)\n",
    "        newfeature_fare = cls_fare.labels_ # the labels from kmeans clustering\n",
    "\n",
    "        # append on the second clustering\n",
    "        cls_fam = KMeans(n_clusters=n_fam, init='k-means++',random_state=1)\n",
    "        cls_fam.fit(X2)\n",
    "        newfeature_fam = cls_fam.labels_ # the labels from kmeans clustering\n",
    "\n",
    "        y = df_imputed['Survived']\n",
    "        X = df_imputed[['IsMale']]\n",
    "        X = np.column_stack((X,pd.get_dummies(newfeature_fare),pd.get_dummies(newfeature_fam)))\n",
    "\n",
    "        acc = cross_val_score(clf,X,y=y,cv=cv)\n",
    "        params.append((n_fare,n_fam,acc.mean()*100,acc.std()*100)) # save state\n",
    "\n",
    "        print (\"Clusters\",n_fare,n_fam,\"Average accuracy = \", acc.mean()*100, \"+-\", acc.std()*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that about the best we can do with these new discretization methods is around 82%. All the models are within one standard deviation of each other, so most clustering in this range are pretty reasonable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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sCccP89krS3HsXhfTdjYHXiYVAnYgPXvi0CA9GKJTzDixlrNOqCUSQVaMiLQVzi7CEqjF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\nnlzs+MLBxlkubG6TId/xkjc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iaYelLdS7WOnonrA9FImdhLmd2NnaszOGUGnEZt0ew8lK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\nB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THsukme2+Qr04P73IDowhlBhucavLrFiU0zOXlsiHkjrFF0cJbJ9MERPu0Qz3H8qHBb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\nnRBvBJ4lZpqLHTMNu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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ef5c828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jgADw7Vx0Tvq2qmRvVzDptLOoje1FLfidYU6NuHBa4yxmdas7sqakPu2y7cFW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\nlJyIiIhIWVFyIiIiImVFyYmIiIiUlf8PNesFg9UULuwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1113db7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_fare=7\n",
    "d_fam=17\n",
    "\n",
    "X1 = df_imputed[['Pclass','Fare']]\n",
    "X2 = df_imputed[['Age','Parch','SibSp']]\n",
    "\n",
    "cls_fare = KMeans(n_clusters=n_fare, init='k-means++',random_state=1)\n",
    "cls_fare.fit(X1)\n",
    "newfeature_fare = cls_fare.labels_ # the labels from kmeans clustering\n",
    "\n",
    "# append on the second clustering\n",
    "cls_fam = KMeans(n_clusters=n_fam, init='k-means++',random_state=1)\n",
    "cls_fam.fit(X2)\n",
    "newfeature_fam = cls_fam.labels_ # the labels from kmeans clustering\n",
    "\n",
    "plt.figure()\n",
    "plt.subplot(1,2,1)\n",
    "X2=X2.values\n",
    "plt.scatter(X2[:, 0], X2[:, 1]+np.random.random(X2[:, 1].shape)/2, c=newfeature_fam, cmap=plt.cm.rainbow, s=20, linewidths=0)\n",
    "plt.xlabel('Age (normalized)'), plt.ylabel('Parch')\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.scatter(X2[:, 0], X2[:, 2]+np.random.random(X2[:, 1].shape)/2, c=newfeature_fam, cmap=plt.cm.rainbow, s=20, linewidths=0)\n",
    "plt.xlabel('Age (normalized)'), plt.ylabel('SibSp')\n",
    "plt.grid()\n",
    "\n",
    "X1=X1.values\n",
    "plt.figure()\n",
    "plt.scatter(X1[:, 1], X1[:, 0]+np.random.random(X1[:, 0].shape)/2, c=newfeature_fare, cmap=plt.cm.rainbow, s=20, linewidths=0)\n",
    "plt.xlabel('Fare (Normalized)'), plt.ylabel('Class')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if we can make things simpler by only clustering on one set of attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clusters 15 Average accuracy =  79.3769152196 +- 3.78087097027\n",
      "Clusters 16 Average accuracy =  79.0360061287 +- 4.28147028905\n",
      "Clusters 17 Average accuracy =  79.2607252298 +- 3.74301427739\n",
      "Clusters 18 Average accuracy =  78.8100102145 +- 4.78260228161\n",
      "Clusters 19 Average accuracy =  78.921092952 +- 3.53091546099\n"
     ]
    }
   ],
   "source": [
    "\n",
    "params = []\n",
    "for n_fam in range(15,20):\n",
    "\n",
    "    # append on the clustering\n",
    "    cls_fam = KMeans(n_clusters=n_fam, init='k-means++',random_state=1)\n",
    "    cls_fam.fit(X2)\n",
    "    newfeature_fam = cls_fam.labels_ # the labels from kmeans clustering\n",
    "\n",
    "    y = df_imputed['Survived']\n",
    "    X = df_imputed[['IsMale','Pclass','Fare']]\n",
    "    X = np.column_stack((X,pd.get_dummies(newfeature_fam)))\n",
    "\n",
    "    acc = cross_val_score(clf,X,y=y,cv=cv)\n",
    "    params.append((n_fare,n_fam,acc.mean()*100,acc.std()*100)) # save state\n",
    "\n",
    "    print (\"Clusters\",n_fam,\"Average accuracy = \", acc.mean()*100, \"+-\", acc.std()*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it seems that the accuracy is fairly stagnant, but has a tight standard deviation. Now, let's also try to replace features using some slightly different clustering algorithms and see what works best for classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C= 13 ward Average accuracy =  80.7328907048 +- 3.16113751286\n",
      "C= 14 ward Average accuracy =  80.393258427 +- 3.50666059037\n",
      "C= 15 ward Average accuracy =  80.39453524 +- 3.063905792\n",
      "C= 16 ward Average accuracy =  80.39453524 +- 3.34594828739\n",
      "C= 17 ward Average accuracy =  80.8490806946 +- 3.35409073854\n",
      "C= 18 ward Average accuracy =  80.2808988764 +- 3.57713351014\n",
      "C= 19 ward Average accuracy =  80.2808988764 +- 3.3918375308\n",
      "C= 13 complete Average accuracy =  80.5081716037 +- 4.4318555186\n",
      "C= 14 complete Average accuracy =  79.606741573 +- 5.46714540835\n",
      "C= 15 complete Average accuracy =  79.9438202247 +- 4.86566910579\n",
      "C= 16 complete Average accuracy =  79.8352911134 +- 5.84143042135\n",
      "C= 17 complete Average accuracy =  79.9463738509 +- 5.51817044614\n",
      "C= 18 complete Average accuracy =  79.7178243105 +- 4.76129827162\n",
      "C= 19 complete Average accuracy =  79.9438202247 +- 4.8621095527\n",
      "C= 13 average Average accuracy =  81.5283452503 +- 3.74922435474\n",
      "C= 14 average Average accuracy =  81.4172625128 +- 4.25344438849\n",
      "C= 15 average Average accuracy =  81.5296220633 +- 3.66844283924\n",
      "C= 16 average Average accuracy =  81.5296220633 +- 4.00501154716\n",
      "C= 17 average Average accuracy =  80.5068947906 +- 4.23107822969\n",
      "C= 18 average Average accuracy =  80.9627170582 +- 4.48367596153\n",
      "C= 19 average Average accuracy =  80.6205311542 +- 4.03114699193\n",
      "CPU times: user 51 s, sys: 131 ms, total: 51.1 s\n",
      "Wall time: 51.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "X1 = df_imputed[['Pclass','Fare']]\n",
    "\n",
    "params = []\n",
    "for link in ['ward', 'complete', 'average']:\n",
    "    for n_fam in range(13,20):\n",
    "\n",
    "        # append on the clustering\n",
    "        cls_fam = AgglomerativeClustering(n_clusters=n_fam, linkage=link)\n",
    "        cls_fam.fit(X2)\n",
    "        newfeature_fam = cls_fam.labels_ # the labels from kmeans clustering\n",
    "\n",
    "        y = df_imputed['Survived']\n",
    "        X = df_imputed[['IsMale','Pclass','Fare']]\n",
    "        X = np.column_stack((X,pd.get_dummies(newfeature_fam)))\n",
    "\n",
    "        acc = cross_val_score(clf,X,y=y,cv=cv)\n",
    "        params.append((n_fare,n_fam,acc.mean()*100,acc.std()*100)) # save state\n",
    "\n",
    "        print (\"C=\",n_fam,link,\"Average accuracy = \", acc.mean()*100, \"+-\", acc.std()*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, all fairly good performances using different types of linkage and also different numbers of clusters. Let's now try DBSCAN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1 5 Average accuracy =  80.6269152196 +- 5.598585979\n",
      "0.1 6 Average accuracy =  81.7594484168 +- 5.30177684228\n",
      "0.1 7 Average accuracy =  81.0814606742 +- 6.0293245421\n",
      "0.125 5 Average accuracy =  80.9678243105 +- 5.71715264265\n",
      "0.125 6 Average accuracy =  81.6458120531 +- 5.16353768423\n",
      "0.125 7 Average accuracy =  81.1950970378 +- 6.0953210737\n",
      "0.15 5 Average accuracy =  80.9678243105 +- 5.71715264265\n",
      "0.15 6 Average accuracy =  81.6458120531 +- 5.16353768423\n",
      "0.15 7 Average accuracy =  81.1950970378 +- 6.0953210737\n",
      "CPU times: user 21.1 s, sys: 59.3 ms, total: 21.2 s\n",
      "Wall time: 21.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "from sklearn.cluster import DBSCAN\n",
    "\n",
    "params = []\n",
    "for eps in [0.1, 0.125, 0.15]:\n",
    "    for mpts in range(5,8):\n",
    "\n",
    "        # append on the clustering\n",
    "        cls_fam = DBSCAN(eps=eps, min_samples=mpts)\n",
    "        cls_fam.fit(X2)\n",
    "        newfeature_fam = cls_fam.labels_ # the labels from kmeans clustering\n",
    "\n",
    "        y = df_imputed['Survived']\n",
    "        X = df_imputed[['IsMale','Pclass','Fare']]\n",
    "        X = np.column_stack((X,pd.get_dummies(newfeature_fam)))\n",
    "\n",
    "        acc = cross_val_score(clf,X,y=y,cv=cv)\n",
    "        params.append((n_fare,n_fam,acc.mean()*100,acc.std()*100)) # save state\n",
    "\n",
    "        print (eps,mpts,\"Average accuracy = \", acc.mean()*100, \"+-\", acc.std()*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that DBSCAN has good clusterings for this dataset that are able to capture some of the nuances for the attributes. Although this is not spatial data, it is interesting that contiguous clustering helps discretize the data a bit (for a small range of eps and minpts). Even so, the center based clustering also tend to do well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing different clusters\n",
    "Now lets take the best performers from each dataset and show the clustering that they found in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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137LWfmitfRS4CJhnjNHbQ5EhbOV/ctqSUh2teCI3qddZ8s88/jVnFP/cfTT3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RuCOxqNtvdAhzNwrwPfJfszYdRIfaE8E2jBvaj+HrhxpN6mIz1LJVpOAsbhBtx/AG611v4i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DpJN4N76KkDapyXOQvsMPjOndsoqyv/8050JoVNbT9YkfYZUeP65wVvxFZHp1QbnwnoAa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\nMCUiIiIiIiIiIqn4P8vRNoFNxssbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e1e8a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "data = X2\n",
    "\n",
    "cls = DBSCAN(eps=0.125, min_samples=6)\n",
    "cls.fit(data)\n",
    "dbs_labels = cls.labels_ \n",
    "\n",
    "cls = AgglomerativeClustering(n_clusters=14, linkage='complete')\n",
    "cls.fit(data)\n",
    "hac_labels = cls.labels_ \n",
    "\n",
    "cls = KMeans(n_clusters=17, random_state=1)\n",
    "cls.fit(data)\n",
    "kmn_labels = cls.labels_\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "title = ['DBSCAN','HAC','KMEANS']\n",
    "\n",
    "for i,l in enumerate([dbs_labels,hac_labels,kmn_labels]):\n",
    "    \n",
    "    plt.subplot(3,2,2*i+1)\n",
    "    plt.scatter(data[:, 0], data[:, 1]+np.random.random(data[:, 1].shape)/2, c=l, cmap=plt.cm.rainbow, s=20, linewidths=0)\n",
    "    plt.xlabel('Age (normalized)'), plt.ylabel('Parch')\n",
    "    plt.grid()\n",
    "    plt.title(title[i])\n",
    "    \n",
    "    plt.subplot(3,2,2*i+2)\n",
    "    plt.scatter(data[:, 0], data[:, 2]+np.random.random(data[:, 1].shape)/2, c=l, cmap=plt.cm.rainbow, s=20, linewidths=0)\n",
    "    plt.xlabel('Age (normalized)'), plt.ylabel('SibSp')\n",
    "    plt.grid()\n",
    "    plt.title(title[i])\n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:MLEnv]",
   "language": "python",
   "name": "conda-env-MLEnv-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
